1. Technical Field
This invention relates to image estimation methods to improve representation of geometric entities within images when noise and outliers contaminate images.
Structure fitting is an important procedure for the measurement of geometrical entities within a digital image because it allows for a non-discrete representation of the image geometrical entity. Using the structure fit representation of the geometrical entity enables accurate measurement. Measurement accuracy achieved with this method usually is better than the pixel pitch contained in the original image. Such accuracy depends on accurate structure fitting to the features in an image. The traditional method of structure fitting is to extract a feature mask image from the original image and then apply the structure fitting technique using the mask portion of the image. This method is sensitive to the noise or distortion in the original image because they corrupt the mask. To overcome this problem, a weight image with weights corresponding to feature signal intensity is created from the original image and the weight image is used for structure fitting. This method improves the robustness of the fitting, however, in the presence of strong noise or outlier, this method is not reliable. Under the prior art method, the noise and outliers degrade measurement accuracy and repeatability more than is necessary.
2. Prior Art
In the prior art approach, high quality of the feature weight image produces a good feature fitting result. Yet prior art suffers the limitations imposed by imperfect images. This invention improves on the prior art by dealing with a broad range of input image imperfections that include noise and outliers.